The Case for Dumb Requirements Engineering Tools

Berry, Daniel M.; Gacitúa, Ricardo; Sawyer, Pete; Tjong, Sri Fatimah

doi:10.1007/978-3-642-28714-5_18

Cited by 56 publications

(51 citation statements)

References 12 publications

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“…Nowadays, the ambitious objective of full automation is considered unattainable in the foreseeable future [8,55]. Therefore, RE research has applied NLP to specific use cases.…”

Section: Nlp For Re: Extracting Models From Requirementsmentioning

confidence: 99%

“…Therefore, RE research has applied NLP to specific use cases. Berry et al [8] categorizes the fundamental approach of all NLP RE tools into four types: In [37], we provide an overview of contemporary tools in NLP for RE and introduce the AQUSA tool for automatically detecting quality defects in user stories. Furthermore, observing that no objective comparison of NLP for RE tools is available, Arendse and Lucassen apply three tools of type I on 112 requirements [5].…”

Section: Nlp For Re: Extracting Models From Requirementsmentioning

confidence: 99%

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Extracting conceptual models from user stories with Visual Narrator

et al. 2017

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Extracting conceptual models from natural language requirements can help identify dependencies, redundancies, and conflicts between requirements via a holistic and easy-to-understand view that is generated from lengthy textual specifications. Unfortunately, existing approaches never gained traction in practice, because they either require substantial human involvement or they deliver too low accuracy. In this paper, we propose an automated approach called Visual Narrator based on natural language processing that extracts conceptual models from user story requirements. We choose this notation because of its popularity among (agile) practitioners and its focus on the essential components of a requirement: Who? What? Why? Coupled with a careful selection and tuning of heuristics, we show how Visual Narrator enables generating conceptual models from user stories with high accuracy. Visual Narrator is part of the holistic Grimm method for user story collaboration that ranges from elicitation to the interactive visualization and analysis of requirements.

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“…Nowadays, the ambitious objective of full automation is considered unattainable in the foreseeable future [8,55]. Therefore, RE research has applied NLP to specific use cases.…”

Section: Nlp For Re: Extracting Models From Requirementsmentioning

confidence: 99%

Section: Nlp For Re: Extracting Models From Requirementsmentioning

confidence: 99%

Extracting conceptual models from user stories with Visual Narrator

et al. 2017

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“…In particular, it should be noted that while easy access to terms' context in the text using concordances makes validation of true positives and rejection of false positives easy, only a painstaking manual analysis can reveal the false negatives. If this needs to be done, it negates any effort-saving advantages of text mining [4]. Nevertheless, text mining did allow us to posit heuristics which may direct future automated analysis of affect-laden applications, e.g.…”

Section: Methodsmentioning

confidence: 99%

“…Here, being able to automatically infer derived_from relationships by the application (e.g.) TF-IDF is attractive, provided the omission of a minority of genuine trace relationships that the tool will fail to identify can be tolerated [4].…”

Section: Related Workmentioning

confidence: 99%

Discovering affect-laden requirements to achieve system acceptance

Sutcliffe

Rayson

Bull

et al. 2014

2014 IEEE 22nd International Requirements Engineering Conference (RE)

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Abstract-Novel envisioned systems face the risk of rejection by their target user community and the requirements engineer must be sensitive to the factors that will determine acceptance or rejection. Conventionally, technology acceptance is determined by perceived usefulness and ease-of-use, but in some domains other factors play an important role. In healthcare systems, particularly, ethical and emotional factors can be crucial. In this paper we describe an approach to requirements discovery that we developed for such systems. We describe how we have applied our approach to a novel system to passively monitor users for signs of cognitive decline consistent with the onset of dementia. A key challenge was eliciting users' reactions to emotionally charged events never before experienced by them at first hand. Our goal was to understand the range of users' emotional responses and their values and motivations, and from these formulate requirements that would maximise the likelihood of acceptance of the system. The problem was heightened by the fact that the key stakeholders were elderly people who represent a poorly studied user constituency. We discuss the elicitation and analysis methodologies used, and our experience with tool support. We conclude by reflecting on the affect issues for RE and for technology acceptance.

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“…Some authors (i.a. [26], [27], [28]) argue that if an approach does not achieve perfect recall, this leads to either the reviewer does not check the rule anymore, which would lead to unchecked defects, or the reviewer has to go through the whole document anyways, and thus, the automated analysis has no benefits. We disagree with this view for two reasons.…”

Section: B the 100%-recall Argumentmentioning

confidence: 99%

Which Requirements Artifact Quality Defects are Automatically Detectable? A Case Study

Femmer

Unterkalmsteiner

Gorschek

2017

2017 IEEE 25th International Requirements Engineering Conference Workshops (REW)

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Abstract-[Context:]The quality of requirements engineering artifacts, e.g. requirements specifications, is acknowledged to be an important success factor for projects. Therefore, many companies spend significant amounts of money to control the quality of their RE artifacts. To reduce spending and improve the RE artifact quality, methods were proposed that combine manual quality control, i.e. reviews, with automated approaches.

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The Case for Dumb Requirements Engineering Tools

Cited by 56 publications

References 12 publications

Extracting conceptual models from user stories with Visual Narrator

Extracting conceptual models from user stories with Visual Narrator

Discovering affect-laden requirements to achieve system acceptance

Which Requirements Artifact Quality Defects are Automatically Detectable? A Case Study

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